Qinyuan Liu

A survey of event-based strategies on control and estimation

The event-based strategies have recently received considerable research attention due primarily to their irreplaceable superiority in resource-constrained systems. Compared with the widely adopted time-driven schemes, such novel event-based schemes have advantages of improving the efficiency in resource utilization in many real applications. Event-based strategies represent an effective way of generating sporadic executions, where an execution is generated only when a specific event (e.g. a certain signal exceeds a prescribed threshold) arises. In this survey, we aim to summarize the results available in the literature on event-based strategies so as to promote the related research in this realm. The progress of the event-based design and analysis strategies is systematically reviewed in both control and estimation domains. Specifically, the event-based control strategies have been discussed for networked control systems, multi-agent systems and other systems, and the event-based estimation schemes have been highlighted according to the send-on-delta and send-on-area concepts. Some potential future research directions are finally pointed out for event-based strategies.

Event-Based Recursive Distributed Filtering Over Wireless Sensor Networks

In this technical note, the distributed filtering problem is investigated for a class of discrete time-varying systems with an event-based communication mechanism. Each intelligent sensor node transmits the data to its neighbors only when the local innovation violates a predetermined Send-on-Delta (SoD) data transmission condition. The aim of the proposed problem is to construct a distributed filter for each sensor node subject to sporadic communications over wireless networks. In terms of an event indicator variable, the triggering information is utilized so as to reduce the conservatism in the filter analysis. An upper bound for the filtering error covariance is obtained in form of Riccati-like difference equations by utilizing the inductive method. Subsequently, such an upper bound is minimized by appropriately designing the filter parameters iteratively, where a novel matrix simplification technique is developed to handle the challenges resulting from the sparseness of the sensor network topology and filter structure preserving issues. The effectiveness of the proposed strategy is illustrated by a numerical simulation.

Event-Based

In this technical note, the H∞ consensus control problem is investigated over a finite horizon for general discrete time-varying multi-agent systems subject to energy-bounded external disturbances. A decentralized estimation-based output feedback control protocol is put forward via the relative output measurements. A novel event-based mechanism is proposed for each intelligent agent to utilize the available information in order to decide when to broadcast messages and update control input. The aim of the problem addressed is to co-design the time-varying controller and estimator parameters such that the controlled multi-agent systems achieve consensus with a disturbance attenuation level γ over a finite horizon [0, T]. A constrained recursive Riccati difference equation approach is developed to derive the sufficient conditions under which the H∞ consensus performance is guaranteed in the framework of event-based scheme. Furthermore, the desired controller and estimator parameters can be iteratively computed by resorting to the Moore-Penrose pseudo inverse. Finally, the effectiveness of the developed event-based H∞ consensus control strategy is demonstrated in the numerical simulation.

H_{\infty}

In this paper, we investigate the distributed filtering problem over wireless sensor networks (WSNs) with bandwidth and energy constraints. To utilize the limited resources efficiently, a novel event-based mechanism is proposed for the sensor node, such that only selected valuable data are broadcasted to its neighboring sensors via the wireless channel according to whether specific events happen. By resorting to graph theory and utilizing stochastic analysis methods, the filter parameters and the event triggering rules are designed, such that the filtering error converges at an exponential rate in the mean square sense. An adaptive algorithm for determining the triggering threshold is developed, which allows the intelligent sensors to tune the boundary of a local event domain in an online manner, so as to keep the average transmission rate level off a desired value. An illustrative example is given to demonstrate the effectiveness of the proposed strategy.

Consensus Control of Multi-Agent Systems With Relative Output Feedback: The Finite-Horizon Case

This paper is concerned with the distributed filtering problem for a class of discrete time-varying systems with stochastic nonlinearities and sensor degradation over a finite horizon. A two-step distributed filter algorithm is proposed where the sensor nodes collaboratively estimate the states of the plant by exploiting the information from both the local and the neighboring nodes. The goal of this paper is to design the distributed filters over a wireless sensor network subject to given sporadic communication topology. Moreover, a resilient operation is guaranteed to suppress random perturbations on the actually implemented filter gains. An upper bound is first derived for the filtering error covariance by utilizing an inductive method and such an upper bound is subsequently minimized via iteratively solving a quadratic optimization problem. To account for the topological information of the sensor networks, a novel matrix simplification technique is utilized to preserve the sparsity of the gain matrices in accordance with the given topology, and the analytical parameterization is obtained for the gain matrices of the desired suboptimal filter. Furthermore, a sufficient condition is established to guarantee the mean-square boundedness of the estimation errors. Numerical simulation is carried out to verify the effectiveness of the proposed filtering algorithm.

Event-Based Distributed Filtering With Stochastic Measurement Fading

This paper is concerned with the distributed filtering problem over wireless sensor networks for a class of state-saturated systems subject to fading measurements and quantization effects. Each sensor node in the network communicates with its neighbors according to the network topology described by a directed graph. The fading phenomena of measurements are assumed to occur in a random way and the attenuation coefficients of the fading measurements are described by a set of random variables with known stochastic properties. By solving two sets of matrix difference equations, an upper bound for the filtering error covariance is presented. Subsequently, with the topology information of the sensor network, such an upper bound is minimized by properly designing the filter parameters. Moreover, the performance of the proposed filter is investigated through establishing sufficient conditions ensuring that the trace of the upper bound is bounded. The relationship between the filter performance and the mean of attenuation coefficient is also discussed. A numerical simulation is exploited to demonstrate the effectiveness of the proposed filtering method.

A Resilient Approach to Distributed Filter Design for Time-Varying Systems Under Stochastic Nonlinearities and Sensor Degradation

Abstract This paper is concerned with the remote state estimation problem for a class of discrete-time stochastic systems. An event-triggered scheme is exploited to regulate the sensor-to-estimator communication in order to preserve limited network resources. A situation is considered where the sensors are susceptible to possible failures and the signals are quantized before entering the network. Furthermore, the resilience issue for the filter design is taken into account in order to accommodate the possible gain variations in the course of filter implementation. In the simultaneous presence of measurement quantizations, sensor failures and gain variations, an event-triggered filter is designed to minimize certain upper bound of the covariance of the estimation error in terms of the solution to Riccati-like difference equations. Further analysis demonstrates the monotonicity of the minimized upper bound with respect to the value of thresholds. Subsequently, a sufficient condition is also established for the convergence of the steady-state filter. A numerical example is presented to verify the effectiveness of the proposed filtering algorithm.

Recursive Distributed Filtering for a Class of State-Saturated Systems With Fading Measurements and Quantization Effects

The state estimation or filtering problem has proven to be one of the fundamental issues in signal processing and control engineering, and a number of algorithms have been proposed in the literature, see, e.g., [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]. Accordingly, a core problem with the widespread applications of wireless sensor networks (WSNs) is to estimate the plant states based on noisy measurement outputs from distributed nodes. A seemingly natural way is to employ the traditional Kalman filters by establishing a fusion center in WSNs in order to collect all the measurements from the individual sensors and then process the measurements in a global sense. Unfortunately, due to the limited communication capability and energy supply, it might be impossible for the sensors to persistently forward the local messages to the fusion center. As such, the so-called distributed estimation scheme would be more preferable whose main idea is to estimate the plant states based on both the local and the neighboring information according to the topologies of WSNs. Recently, various types of consensus protocols have been proposed with an aim to improve the efficiency of the distributed computation and a rich body of literature has been available on the consensus-based distributed filtering strategies, see, e.g., the seminal work in [11].

Event-triggered resilient filtering with measurement quantization and random sensor failures: Monotonicity and convergence

The state estimation or filtering problem has long been a fundamental research issue in control engineering and signal processing with tremendous application insights in almost all practical systems especially in guidance, navigation, and vehicle control [1, 2].

A Resilient Approach to Distributed Recursive Filter Design

Over the past decades, wireless sensor networks have attracted increasing research attention due primarily to their potential applications in various realms including seismic sensing, battlefield surveillance, intelligent transportation, and machine health monitoring by Akyildiz et al. (Comput. Netw. 38(4):393–422 (2002), [1]). A typical sensor network is composed of a group of autonomous sensor nodes spatially disseminated over certain monitored regions. Each sensor node shares the local information over the network via wireless communication to help the system complete complicated tasks in a cooperative manner.